import numpy as np import pytest from pandas import ( DataFrame, NaT, date_range, ) import pandas._testing as tm @pytest.fixture def float_frame_with_na(): """ Fixture for DataFrame of floats with index of unique strings Columns are ['A', 'B', 'C', 'D']; some entries are missing A B C D ABwBzA0ljw -1.128865 -0.897161 0.046603 0.274997 DJiRzmbyQF 0.728869 0.233502 0.722431 -0.890872 neMgPD5UBF 0.486072 -1.027393 -0.031553 1.449522 0yWA4n8VeX -1.937191 -1.142531 0.805215 -0.462018 3slYUbbqU1 0.153260 1.164691 1.489795 -0.545826 soujjZ0A08 NaN NaN NaN NaN 7W6NLGsjB9 NaN NaN NaN NaN ... ... ... ... ... uhfeaNkCR1 -0.231210 -0.340472 0.244717 -0.901590 n6p7GYuBIV -0.419052 1.922721 -0.125361 -0.727717 ZhzAeY6p1y 1.234374 -1.425359 -0.827038 -0.633189 uWdPsORyUh 0.046738 -0.980445 -1.102965 0.605503 3DJA6aN590 -0.091018 -1.684734 -1.100900 0.215947 2GBPAzdbMk -2.883405 -1.021071 1.209877 1.633083 sHadBoyVHw -2.223032 -0.326384 0.258931 0.245517 [30 rows x 4 columns] """ df = DataFrame(tm.getSeriesData()) # set some NAs df.iloc[5:10] = np.nan df.iloc[15:20, -2:] = np.nan return df @pytest.fixture def bool_frame_with_na(): """ Fixture for DataFrame of booleans with index of unique strings Columns are ['A', 'B', 'C', 'D']; some entries are missing A B C D zBZxY2IDGd False False False False IhBWBMWllt False True True True ctjdvZSR6R True False True True AVTujptmxb False True False True G9lrImrSWq False False False True sFFwdIUfz2 NaN NaN NaN NaN s15ptEJnRb NaN NaN NaN NaN ... ... ... ... ... UW41KkDyZ4 True True False False l9l6XkOdqV True False False False X2MeZfzDYA False True False False xWkIKU7vfX False True False True QOhL6VmpGU False False False True 22PwkRJdat False True False False kfboQ3VeIK True False True False [30 rows x 4 columns] """ df = DataFrame(tm.getSeriesData()) > 0 df = df.astype(object) # set some NAs df.iloc[5:10] = np.nan df.iloc[15:20, -2:] = np.nan # For `any` tests we need to have at least one True before the first NaN # in each column for i in range(4): df.iloc[i, i] = True return df @pytest.fixture def float_string_frame(): """ Fixture for DataFrame of floats and strings with index of unique strings Columns are ['A', 'B', 'C', 'D', 'foo']. A B C D foo w3orJvq07g -1.594062 -1.084273 -1.252457 0.356460 bar PeukuVdmz2 0.109855 -0.955086 -0.809485 0.409747 bar ahp2KvwiM8 -1.533729 -0.142519 -0.154666 1.302623 bar 3WSJ7BUCGd 2.484964 0.213829 0.034778 -2.327831 bar khdAmufk0U -0.193480 -0.743518 -0.077987 0.153646 bar LE2DZiFlrE -0.193566 -1.343194 -0.107321 0.959978 bar HJXSJhVn7b 0.142590 1.257603 -0.659409 -0.223844 bar ... ... ... ... ... ... 9a1Vypttgw -1.316394 1.601354 0.173596 1.213196 bar h5d1gVFbEy 0.609475 1.106738 -0.155271 0.294630 bar mK9LsTQG92 1.303613 0.857040 -1.019153 0.369468 bar oOLksd9gKH 0.558219 -0.134491 -0.289869 -0.951033 bar 9jgoOjKyHg 0.058270 -0.496110 -0.413212 -0.852659 bar jZLDHclHAO 0.096298 1.267510 0.549206 -0.005235 bar lR0nxDp1C2 -2.119350 -0.794384 0.544118 0.145849 bar [30 rows x 5 columns] """ df = DataFrame(tm.getSeriesData()) df["foo"] = "bar" return df @pytest.fixture def mixed_float_frame(): """ Fixture for DataFrame of different float types with index of unique strings Columns are ['A', 'B', 'C', 'D']. A B C D GI7bbDaEZe -0.237908 -0.246225 -0.468506 0.752993 KGp9mFepzA -1.140809 -0.644046 -1.225586 0.801588 VeVYLAb1l2 -1.154013 -1.677615 0.690430 -0.003731 kmPME4WKhO 0.979578 0.998274 -0.776367 0.897607 CPyopdXTiz 0.048119 -0.257174 0.836426 0.111266 0kJZQndAj0 0.274357 -0.281135 -0.344238 0.834541 tqdwQsaHG8 -0.979716 -0.519897 0.582031 0.144710 ... ... ... ... ... 7FhZTWILQj -2.906357 1.261039 -0.780273 -0.537237 4pUDPM4eGq -2.042512 -0.464382 -0.382080 1.132612 B8dUgUzwTi -1.506637 -0.364435 1.087891 0.297653 hErlVYjVv9 1.477453 -0.495515 -0.713867 1.438427 1BKN3o7YLs 0.127535 -0.349812 -0.881836 0.489827 9S4Ekn7zga 1.445518 -2.095149 0.031982 0.373204 xN1dNn6OV6 1.425017 -0.983995 -0.363281 -0.224502 [30 rows x 4 columns] """ df = DataFrame(tm.getSeriesData()) df.A = df.A.astype("float32") df.B = df.B.astype("float32") df.C = df.C.astype("float16") df.D = df.D.astype("float64") return df @pytest.fixture def mixed_int_frame(): """ Fixture for DataFrame of different int types with index of unique strings Columns are ['A', 'B', 'C', 'D']. A B C D mUrCZ67juP 0 1 2 2 rw99ACYaKS 0 1 0 0 7QsEcpaaVU 0 1 1 1 xkrimI2pcE 0 1 0 0 dz01SuzoS8 0 1 255 255 ccQkqOHX75 -1 1 0 0 DN0iXaoDLd 0 1 0 0 ... .. .. ... ... Dfb141wAaQ 1 1 254 254 IPD8eQOVu5 0 1 0 0 CcaKulsCmv 0 1 0 0 rIBa8gu7E5 0 1 0 0 RP6peZmh5o 0 1 1 1 NMb9pipQWQ 0 1 0 0 PqgbJEzjib 0 1 3 3 [30 rows x 4 columns] """ df = DataFrame({k: v.astype(int) for k, v in tm.getSeriesData().items()}) df.A = df.A.astype("int32") df.B = np.ones(len(df.B), dtype="uint64") df.C = df.C.astype("uint8") df.D = df.C.astype("int64") return df @pytest.fixture def timezone_frame(): """ Fixture for DataFrame of date_range Series with different time zones Columns are ['A', 'B', 'C']; some entries are missing A B C 0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00 1 2013-01-02 NaT NaT 2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00 """ df = DataFrame( { "A": date_range("20130101", periods=3), "B": date_range("20130101", periods=3, tz="US/Eastern"), "C": date_range("20130101", periods=3, tz="CET"), } ) df.iloc[1, 1] = NaT df.iloc[1, 2] = NaT return df @pytest.fixture def uint64_frame(): """ Fixture for DataFrame with uint64 values Columns are ['A', 'B'] """ return DataFrame( {"A": np.arange(3), "B": [2**63, 2**63 + 5, 2**63 + 10]}, dtype=np.uint64 ) @pytest.fixture def simple_frame(): """ Fixture for simple 3x3 DataFrame Columns are ['one', 'two', 'three'], index is ['a', 'b', 'c']. one two three a 1.0 2.0 3.0 b 4.0 5.0 6.0 c 7.0 8.0 9.0 """ arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]) return DataFrame(arr, columns=["one", "two", "three"], index=["a", "b", "c"]) @pytest.fixture def frame_of_index_cols(): """ Fixture for DataFrame of columns that can be used for indexing Columns are ['A', 'B', 'C', 'D', 'E', ('tuple', 'as', 'label')]; 'A' & 'B' contain duplicates (but are jointly unique), the rest are unique. A B C D E (tuple, as, label) 0 foo one a 0.608477 -0.012500 -1.664297 1 foo two b -0.633460 0.249614 -0.364411 2 foo three c 0.615256 2.154968 -0.834666 3 bar one d 0.234246 1.085675 0.718445 4 bar two e 0.533841 -0.005702 -3.533912 """ df = DataFrame( { "A": ["foo", "foo", "foo", "bar", "bar"], "B": ["one", "two", "three", "one", "two"], "C": ["a", "b", "c", "d", "e"], "D": np.random.default_rng(2).standard_normal(5), "E": np.random.default_rng(2).standard_normal(5), ("tuple", "as", "label"): np.random.default_rng(2).standard_normal(5), } ) return df